Khushiyant Chauhan
Data Scientist at Turing
Delhi, India
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Khushiyant Chauhan, is MLOps Engineer at Quantum Leap Labs as well as research assistant in Norwegian University of Science and Technology in field of Neuroscience.
He has elaborative experience as Developer being MLOps engineer at Wains.me and even, being a Core contributor at Zulip, Ivy and maintainer of popular community such as Collabnix (for Container tech in academia section) and Docker Delhi. Khushiyant, has been the co-founder of Sibilize, providing personality based living spaces for students.
As a speaker, Khushiyant has shared his knowledge and experience at conferences such as the Global AI Bootcamp in London, GGSIPU in India, Upcoming DWX23 Germany ,Upcoming Docker Delhi'23 and many more
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Clear Skies Ahead: Navigating Cloud Monitoring
Have you ever attended a Cloud monitoring presentation and walked away with more questions than answers? This session will finally provide practical guidance on how it actually cloud system monitoring works and what are its requirements.We’ll cover what you’ll need to build out your own cloud infrastructure and monitor its resources, security etc.
Some of the topics that we'll explore are:
- Deploying dummy services (for further hands-on practice)
- How attackers exploit poor infrastructure
- Objectives of Cloud monitoring
- What to monitor such as Error rate, latency, reports, traffic
- Optimising Cost
- Use of various tech such as k8s, docker, datadog
This will benefit both newcomers and seasoned professionals of the DevOps field by making them learn and remember the cloud monitoring importance respectively.
Role of DevOps in deployment and maintenance of ML models
In this speaker session, we will explore the role of DevOps in the deployment and maintenance of machine learning models. We will discuss how DevOps practices such as continuous integration, continuous deployment, and infrastructure as code can be applied to the ML workflow to improve collaboration, efficiency, and scalability. Attendees will learn about tools and techniques for automating the deployment and monitoring of ML models, as well as strategies for managing and updating models in production. The session will also cover best practices for testing, debugging, and troubleshooting ML models, with a focus on maintaining high accuracy and performance in production environments. By the end of the session, attendees will have a better understanding of how to leverage DevOps to optimize the deployment and maintenance of ML models.
Session will likely consists of the following segments:
- Explanation of how DevOps practices can improve collaboration, efficiency and scalability in ML model deployment and maintenance
- Discussion on the importance of version control and configuration management in ML models
Understanding of the role of containers and container orchestration in ML model deployment
- Explanation of how to implement Continuous integration and Continuous Deployment for ML models
- Overview of monitoring and logging practices specific to ML models
- Strategies for A/B testing and rollbacks in ML model deployment
- Best practices for maintaining model performance in production
- Understanding of how to manage model drift and retraining in production environments
- Discussions on how to handle data privacy and security in ML model deployment and maintenance.
Khushiyant Chauhan
Data Scientist at Turing
Delhi, India
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